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. 2015:2015:853461.
doi: 10.1155/2015/853461. Epub 2015 Aug 10.

An Affinity Propagation-Based DNA Motif Discovery Algorithm

Chunxiao Sun  1 , Hongwei Huo  1 , Qiang Yu  1 , Haitao Guo  1 , Zhigang Sun  1
Affiliations

An Affinity Propagation-Based DNA Motif Discovery Algorithm

Chunxiao Sun et al. Biomed Res Int. 2015.

Abstract

The planted (l, d) motif search (PMS) is one of the fundamental problems in bioinformatics, which plays an important role in locating transcription factor binding sites (TFBSs) in DNA sequences. Nowadays, identifying weak motifs and reducing the effect of local optimum are still important but challenging tasks for motif discovery. To solve the tasks, we propose a new algorithm, APMotif, which first applies the Affinity Propagation (AP) clustering in DNA sequences to produce informative and good candidate motifs and then employs Expectation Maximization (EM) refinement to obtain the optimal motifs from the candidate motifs. Experimental results both on simulated data sets and real biological data sets show that APMotif usually outperforms four other widely used algorithms in terms of high prediction accuracy.

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Figures

Figure 1
Figure 1
AP clustering results.
Figure 2
Figure 2
Sequence logos of the predicted motifs.
Figure 3
Figure 3
Prediction accuracy on real biological data.
Figure 4
Figure 4
Prediction accuracy on Tompa data.
Algorithm 1
Algorithm 1
APMotif.
See this image and copyright information in PMC

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